Many enterprises use customer contact centers staffed by customer service agents (also referred to as customer service representatives or customer service associates) to interact with customers and provide customer support. A contact center may serve as one gateway for customer service interactions and the enterprise may also utilize resources outside of the contact center, such as a team of back-office employees, to process the interactions. In some cases, the back-office team may be much larger than the contact center team, and may include skilled workers paid at a higher salary. These skilled workers (referred to as knowledge workers) generally have the training and expertise to handle customer interactions, such as fulfilling customer service requests.
However, service request processing and workload distribution techniques for contact centers often result in several inefficiencies. For example, work may be arbitrarily distributed to employees based on rudimentary factors such as whether or not an employee is currently available. In addition, human latencies may exist when employees of the enterprise (e.g., supervisors) set the pace of work and manage the priorities. For example, manual allocation of work by supervisors can result in inefficiencies due to time spent manually selecting tasks and distributing them across many employees' workbins. Managers and supervisors may also experience difficulty gaining insight into resource availability and work distribution, due to the inaccuracies and subjectivity associated with employees' “self-reported” data. Continuous improvement in workload distribution is hindered by this limited insight. Further, customers may experience frustration with long wait times and inefficient customer service. An enterprise's inability to meet customer expectations and commitments (e.g., due dates and response times) may result in the customer ending the business relationship.
In addition, enterprises may experience a lack of business agility by being unable to respond to market opportunities that arise during customer interactions. For instance, enterprises may want to direct offers regarding particular products or services (either within or across departments or brands) to certain customers, but standard interaction processing systems lack the ability to leverage customer interactions to take advantage of these market opportunities.
Accordingly, there is a need to reduce human latency in workflows, optimize utilization of employees in terms of occupancy rate and skills/proficiency, increase visibility into resources and availability, improve the quality of customer outcomes, and improve business agility.
A workload distribution system may employ various techniques to address the issues of reducing human latency in workflows, optimizing employee pool utilization, increasing visibility into workload and teams, and improving the quality of customer service outcomes. For example, rules may be designed to distribute tasks to employees based on routing strategies employed by routing servers for the contact center. However, such rules may need to be changed, depending on traffic flow and the available resources of the contact center, which can fluctuate throughout the day. In some cases, contact centers will need to re-program routing strategies frequently, which may require software expertise. Accordingly, there is a need for a system that permits enterprises to quickly and efficiently respond to changing business conditions by making dynamic decisions about how to handle interactions with customers, without the need to re-program.